Enhanced UAV Navigation in Cluttered Environments via a BS-CYOLOv5 Multi-Sensor Approach

Enhanced UAV Navigation in Cluttered Environments via a BS-CYOLOv5 Multi-Sensor Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhanced UAV Navigation in Cluttered Environments via a BS-CYOLOv5 Multi-Sensor Approach Chahira CHERIF, Mohammed MAIZA, Samira CHOURAQUI, Abdelmalik TALEB-AHMED This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7605109/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Unmanned Aerial Vehicles (UAVs) are critical for applications like aerial surveillance, disaster response, and urban air mobility. Operating safely in these dynamic environments requires robust autonomous navigation and obstacle avoidance capabilities. While traditional methods depend on predefined rules and sensor-based heuristics, they often lack the real-time adaptability needed for complex scenarios. To address this limitation, we propose BS-CYOLOv5, a novel framework that integrates a Deep Learning (DL)-based Backtracking Search-optimized Customized YOLOv5 architecture. This system unifies multi-sensor data processing, real-time obstacle detection, and dynamic path planning. The model was trained and evaluated on the UAV Autonomous Navigation Dataset from Kaggle, consisting of 10000 labeled UAV flight samples gathered from RGB cameras, LiDAR, IMU, and GPS sensors. The data was split into 80% for training, 10% for validation, and 10% for testing, with min-max normalization applied to improve model performance and generalization. For obstacle detection, the CYOLOv5 model provides high-accuracy, real-time identification. The Backtracking Search Algorithm (BSA) then optimizes navigation by dynamically recalibrating flight paths for efficiency and collision avoidance while simultaneously fine-tuning the detection model's hyperparameters. Experimental results demonstrate the framework's effectiveness, showing statistically significant improvements over state-of-the-art baselines. The model achieved 98.10% obstacle detection accuracy, alongside high precision (97.52%), recall (97.54%), F1-score (97.28%), and Intersection over Union (96.1%) metrics. Comprehensive ablation studies and cross-validation confirmed the robustness of our approach. This work contributes to the advancement of intelligent UAV systems by successfully merging state-of-the-art DL with evolutionary optimization, paving the way for greater autonomy and safety in real-world operations. Unmanned Aerial Vehicles Autonomous Navigation Obstacle Avoidance BS-CYOLOv5 Deep Learning Backtracking Search Optimization. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviews received at journal 08 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers invited by journal 22 Sep, 2025 Editor invited by journal 22 Sep, 2025 Editor assigned by journal 22 Sep, 2025 Submission checks completed at journal 19 Sep, 2025 First submitted to journal 19 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7605109","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524891238,"identity":"91d2f9ce-2921-434c-b3a9-f3d976ba3115","order_by":0,"name":"Chahira 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Operating safely in these dynamic environments requires robust autonomous navigation and obstacle avoidance capabilities. While traditional methods depend on predefined rules and sensor-based heuristics, they often lack the real-time adaptability needed for complex scenarios. To address this limitation, we propose BS-CYOLOv5, a novel framework that integrates a Deep Learning (DL)-based Backtracking Search-optimized Customized YOLOv5 architecture. This system unifies multi-sensor data processing, real-time obstacle detection, and dynamic path planning. The model was trained and evaluated on the UAV Autonomous Navigation Dataset from Kaggle, consisting of 10000 labeled UAV flight samples gathered from RGB cameras, LiDAR, IMU, and GPS sensors. The data was split into 80% for training, 10% for validation, and 10% for testing, with min-max normalization applied to improve model performance and generalization. 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